Algodocs

deep learning

intelligent document processing

IDP Use Case: Transforming Restoration Practices

Earthquakes, hurricanes, mudslides, electrical fires, and burst pipes are some of the natural incidences that are usually unforeseen. Buildings that are often at the receiving end during catastrophic calamities require immense repair work. Any comprehensive structure will, in this respect, indeed call for disaster recovery management, especially if it is a house or a company. It has scaled tremendously to be vital software in the construction industry, but it is notably critical in the catastrophe repair industry, where most companies are small. Table of Contents: But as structures progress, many organizations like yours struggle to cope with change. Often, the driving force of success is in the technology that forms the basis of these organizations. Let us think of the building and restoration industry and see what we come up with. These companies have to estimate all possible costs for a building construction project, including additional costs such as salary for office employees, wear and tear of equipment, office rent and other overhead expenses, and cost of all the materials used and wages to workers. Enhancing Data Management in Restoration Processes Intelligent Document Processing (IDP) tools can capture information from any format that has not been pre-formatted, including images and handwritten writings. This can be of great importance, especially in restoration processes where data could be in large quantities, in the form of field notes and sketches, among other things. The Importance of Accurate Costs and Expenses Some of the factors one needs to understand well to accurately estimate the cost of the project include the building material costs, the requirements, the procedures, and the codes that are to be followed, as well as the need to understand the market trends in terms of pricing. Such information may be found by analyzing a bid package and working through the contingencies and profit inherent in a bid or a given project. Two Significant Challenges: Documentation obstacles: One of the challenges associated with restoration events is collecting all the relevant and non-concocted paperwork. Some of the effects of this cumbersome procedure include the failure to complete some forms or the delay in completing them. Accounts Receivable Delays are attributed primarily to fourteen struggles stemming from a high turnover rate in accounting: payment cycles take longer. This not only impacts cash flow but also definitely causes a lot of headaches for the business’s dealings with its customers. The Impact of Intelligent Document Processing (IDP) Software on Restoration Businesses: Implementing Intelligent Document Processing (IDP) Several Challenges. Here are some of the key ones: Case Study Use case for restoration: The repair company wishes to provide the customer with an overview of line-item estimates for the job. Cost estimates should not be utilized as a list of negotiable items. Supplemental costs may apply if more damage or repair that has not been found or is hidden below present finishes is required. This also enables the client to correct himself or herself if they chose compositions that are not within the estimate or if they need extra work. This is because, in the course of the project implementation, changes will be made to adjust for the revised estimate and present it to the client. Any changes made to these documents will be recorded in a change order and presented to the customer for revision. System: It is a form of advanced digital document processing with natural language processing, multimedia processes, and feature extraction. Primary actor: Accountant/bookkeeper/Customer Scenario: To meet the customer’s request to extract the estimated line-item information from the final amount, the following processes should be considered: They ask for the extracted data to be formatted differently than the original document’s formatting. They clearly explain what should be ignored and what needs to be extracted. There are a total of 11 headers in the PDF; each row value contains three different pieces of information: one is the labor, the second is the material, and the third is the equipment information. They need to extract the labor and material information. For example, the following are the instructions for the needed to be extracted data and how it should look like output: Tabular output with headers and the order they should be in JSON form: ·”ITEM#”, mapped from the label in Yellow (as in the picture above) ·”ROOM”, mapped from the label in dark green (as in the picture above) ·”UNIT”, mapped from the label in Red (as in the picture above) ·”QTY”, mapped from the label in light blue (as in the picture above) ·”UNIT PRICE”, mapped from the label in light green (as in the picture above) ·”TOTAL” mapped from the label in pink/magenta (as in the picture above) However, we still need to extract and differentiate data for Labor and Material information. While mapping the extracted data to the new headers, as requested by the customer. As complicated as it looks and sounds, Algodocs can do exactly this request easily.  How to Use Algodocs to Extract We only need a sample file uploaded to Algodocs to create the extractor. There are many ways to upload a sample document. The user can automate importing files to Algodocs uploading from their device, business email, Gmail, or other cloud storage. Once the documents are uploaded, the system will extract data from your documents using Algodocs’ advanced AI engine without relying on templates or even labeling and training your files.  The results are the actual contents extracted from the sample document according to the rules you specify. We have Rule-based extraction and artificial intelligence mining, which can be integrated to synthesize both extraction methods. This can help you further improve your extracted data by putting it into the correct form and structure.  The system makes it easy to control extracted data from your documents and handle business exceptions. It allows you to export the extracted data directly to an Excel Spreadsheet or, with the integration of Zapier, automate exporting extracted data directly to your email, Google Sheets, or other cloud storage. Example of Output in Excel Key Takeaways This should explain how Algodocs has boosted the restoration business and its experience with the solution to prove that technology can transform any business. Let your team be an example of how adopting effective and progressive concepts can

Data Extraction, PDF

What is a PDF Parser?

Back to Blog Table of contents On this page What is a PDF Parser? Home › Blog › Data Extraction › What is a PDF Parser? Categories Data Extraction PDF Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 12:03 Updated July 29, 2026, 02:32 A PDF Parser is a program or a library that enables end-users and organizations to parse data from native PDF documents. Often, organizations need to parse PDF documents for specific fields such as Account Number, Date, Address, Bill to/from information, or parse tabular data. PDF Parsers are usually needed and used for processing and parsing data from large amounts of documents. On the other hand, when you have a handful of documents you simply go and copy the data you need from PDF documents manually and paste it to Excel or anywhere you need it to be. PDF Parsers enable end-users to get data from hundreds and thousands of PDF documents in real time by saving huge amounts of time and, thus, money. Parsing pdf documents isn’t an easy task. There are various ways native pdf documents are generated and parsing data from such pdf documents requires smart approaches. Parsed data from PDF documents greatly varies depending on the industry, which means the data parsed might also greatly change, which complicates the task. Parsing PDF medical forms, which contain specific fields such as First, Middle, and Last names, Sex, Date of Birth, etc. are very different from PDF purchase orders that contain mainly the items in the tabular form with such columns as Item No, Code, Quantity, Item Price, Amount, etc. Therefore, if PDF Parser produces just a bunch of text from a PDF document it does not make much sense for the end-user. What end-users or organizations require is the structured data parsed from PDF documents. In other words, PDF Parser should extract from PDF documents only the data the end-user needs and in the right structured format. For this, the PDF Parser must be smart and flexible enough to parse PDF documents with various layouts and data types. How to parse PDF documents with various layouts? Algodocs allows you to parse PDF documents of any complexity in their layouts and type of data. With the flexible extracting rules of Algodocs, you can parse data from PDFs with different layouts. It is very easy and quick to set up extractors in Algodocs for your PDF documents. We provide 100% free technical support and are ready to set up extractors for you. While we provide free support for creating extracting rules, you may check our help and support section if you wish to learn how to create extracting rules in Algodocs. Watch the following introductory video to get an idea of how it works in Algodocs. Feel free to start a free subscription right now and parse your pdf documents. You can use Algodocs free forever with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. If you have specific requirements and need a custom solution, please contact us. You Might Also Like PDF Data Extraction: The Best Tool and Techniques PDFs are ubiquitous in every organization, serving as the go-to format for sharing and exchanging business data. However, extracting, editing, or… Shubhankar Biswas October 13, 2025 PDF Image Extraction: A Comprehensive Guide To Extracting Image Data From Scanned Pdf Files In 2025 PDF image extraction is a challenging process. Without proper tools and technology, this process can be tedious and prone to errors, which can… Shubhankar Biswas September 1, 2025 Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Table of Contents Introduction Organizations in various industries widely use PDF documents, since no doubt PDF is a common document format for… Shubhankar Biswas October 13, 2025 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

PDF

Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects

Back to Blog Table of contents On this page Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Home › Blog › PDF › Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Categories PDF Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:59 Updated July 21, 2026, 05:15 Introduction Organizations in various industries widely use PDF documents, since no doubt PDF is a common document format for businesses to transfer data. Purchase orders, Invoices, Agreements, and many more document types are interchanged in PDF formats. On the other hand, JSON is another format that represents data in a structured format, which is widely used in transferring data between web applications. As a result, Working with JSON is much easier than with PDF. Therefore, in this article, we will talk about PDF and JSON formats and how you can convert your PDF documents to JSON format. What is a PDF? PDF (Portable Document Format) was initially developed by Adobe® Systems in 1992 and is standardized as ISO 32000. What makes PDF so popular is it is independent of the application software, hardware, and operating system. Other than text and images PDF files may contain a variety of content such as annotations, form fields, layers, etc. There are many advantages of PDF format such as multi-dimensionality, which we have already mentioned – being able to contain various types of content, text, images, videos, vector graphics, interactive fields, hyperlinks, and buttons. Moreover, PDF documents are easily created and viewed on different devices.  Security in PDF was one of the primary concerns of Adobe® Systems. Therefore, PDFs have different access levels to protect the content and the whole document, such as passwords, digital signatures, and watermarks. However, some of the downsides of a PDF are the complexity of editing and especially extracting data from it. Moreover, PDFs are not generated in the same way, so different PDF files can be created in various ways, which complicates the task of extracting data from PDF documents. What is a JSON? JSON (JavaScript Object Notation) is a very popular data format, which appeared in the early 2000s. JSON is a language-independent data format and is used to transfer data between software applications, particularly web applications, usually between server and client.  Most of the API integrations are realized using JSON format for data transfer since it is very easy to work with JSON. Consider a JSON object called person, which contains the following information:{   “name”: “John”,   “surname”: “Doe”,   “age”: 25} Accessing fields of a JSON object is as simple as using the name of the object and the field name you want to access by separating them with a dot as follows: To access a person’s name we use person.name, which will give us “John” as a result. Similarly, we do for surname and age fields: person.surname, person.age Note how easy it is to access any field of a JSON object, which is definitely not compared to accessing specific information in the PDF document. How does JSON differ from PDF? Although PDF and JSON are both widely spread and used, there is a huge difference between PDF and JSON. The difference between them is simply in the purpose of their usage. PDF is mainly used for exchanging information between humans, since it contains text, graphics, illustrations such as images and videos, etc. On the other hand, JSON is mainly used between computer programs and different applications for communicating and exchanging data between each other. It is not an easy task for a human to read information from a JSON file, especially if it is a compressed one, but it is a perfect way to access information from JSON for a software application. The opposite goes for the PDF. Therefore, PDF and JSON become important, useful, and helpful only when they are used in the right place and for the right purpose. How to Convert PDF to JSON? Often, organizations need to transfer data to other programs for further processing. This data is often stored in PDF documents since businesses often speak to each other in a “PDF language”. However, extracting information from PDF documents can be challenging.  The simplest solution is that you can always copy and paste text from a PDF and send it to where it belongs. However, this simple approach has many problems, since first of all this will work only with native PDF files (not scans) for which you can even use some free PDF Parsers. Another problem even if your PDF documents are all native, it is not easy to copy the entire table from a PDF by maintaining its format, especially if the table spans over multiple pages, for example, 100 or 1000 pages. Additionally, often organizations need to extract specific data from PDFs, for example not the entire table, but instead specific rows or columns based on some conditions. Last, but not least, it is not worth spending your valuable time on menial data entry! Convert PDF documents to JSON with Algodocs Algodocs offers a perfect solution to extract any type of data from PDF documents and transfer it to other programs in real-time. Algodocs can extract fields and tables of any complexity from native as well as scanned PDF documents. You can convert your PDF documents to JSON in three steps with Algodocs. First, start by creating an extractor in Algodocs. Algodocs has some preprocessing operations that take some time depending on the number of pages your PDF document contains; usually, it is around 15-20 seconds. Then, go to the ‘Extracting Rules’ editor to create your extracting rules for every field you need to extract from your PDF documents. Similarly, if you need to extract tables from your PDF documents you can create extracting rules for tables by selecting ‘Table’ as the data type. After you are done with creating and extracting rules,

Algodocs

Extract handwritten text from scanned PDFs and images

Back to Blog Table of contents On this page Extract handwritten text from scanned PDFs and images Home › Blog › Algodocs › Extract handwritten text from scanned PDFs and images Categories Algodocs Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:56 Updated November 10, 2025, 13:34 Optical Character Recognition (OCR) engines are primarily focused on machine-printed text and may produce low accuracy for handwritten text. Intelligent Character Recognition (ICR) is an advanced recognition system that is used to recognize handwritten text. This allows the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. Although many processes involve computer-based operations and are implemented in a digital environment, paper is still vastly used across most core business processes such as mortgage origination, order fulfillment, contracts, and other documents that usually require handwritten input and signatures. Nowadays, the digitalization of paper documents plays an important role, and deciding on the right data capture software is critical since handwriting recognition unlike printed text recognition is a more complex task that usually involves advanced deep learning algorithms. Algodocs: Deep Learning Handwriting Recognizer Algodocs is capable of converting handwritten text into machine-printed text with high accuracy. With the ICR of Algodocs, you can automate your document processing workflow and get rid of manual data entry. Scan your paper documents with handwritten text and let Algodocs automatically extract data and convert it to Excel or JSON. Let’s consider the following portion of a scanned document, which contains a table of two columns filled with handwritten numbers. If you upload this image to your account at Algodocs, you will see the following output, which has 100% accuracy. Algodocs uses advanced ICR engines trained with Artificial Intelligence algorithms and Deep Learning. The above example includes mostly digits. Another example with characters is given below. The following is the extracted text by Algodocs from the above image. As we can see Algodocs performs well in handwritten text extraction from scanned documents. Feel free to start a free subscription right now and test your handwritten scanned documents. You can use Algodocs free forever with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. If you have specific requirements and need a custom solution, please contact us. You Might Also Like How to Extract Handwritten Data from PDFs with Algodocs? Table of Contents Want to learn how to extract handwritten data from PDFs easily for your business documents? Algodocs is a powerful AI platform… Shubhankar Biswas October 13, 2025 Conquer Multilingual Data Extraction: A Step-by-Step Guide with Algodocs Introduction Sick of spending hours manually extracting data from multilingual PDFs and images? It could be more efficient, prone to mistakes, and,… Shubhankar Biswas October 12, 2025 Why convert PDF to Word on MacBook (Mac)? Picture this: You have a PDF file that needs to be revised or a scanned document that you wish to convert into an editable format like an MS Word… Shubhankar Biswas October 12, 2025 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

Image Data Extraction

A Guide on Extracting Tables From Low-Quality Scanned Documents

Back to Blog Table of contents On this page A Guide on Extracting Tables From Low-Quality Scanned Documents Home › Blog › Image Data Extraction › A Guide on Extracting Tables From Low-Quality Scanned Documents Categories Image Data Extraction Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:52 Updated July 22, 2026, 09:54 Many companies deal with thousands of documents every month. Document workflow automation becomes vital for such companies as the number of documents increases. One of the most frequent and at the same time tedious operations when processing documents is reading data from tables, especially when documents are scanned PDFs or images. Automating table extraction from scanned documents and exporting them into Excel or JSON within seconds is a dream for every company dealing with manual data entry. Automating table data extraction from scanned documents and images reduces operational costs and saves a lot of time. In this article, we will talk about table extraction from scanned documents or images with low quality. You, most probably, came across some online tools that can extract tabular data from documents. However, there are a few that really work with low-quality scanned documents or images taken by a mobile device. Optical Character Recognition (OCR) is the technology used for converting scanned images into text. However, standard OCR tools require you to apply certain image processing operations on the images before you can apply OCR on them. Without manual pre-processing, OCR will fail in most cases, and accuracy will be low. Unfortunately, even with pre-processing operations free OCR tools produce poor performance. How to extract tables from scanned PDFs and images with low quality? Algodocs has an advanced AI-powered OCR engine that automatically handles any type of scanned PDF or image with a low quality. Algodocs accepts either colorful scanned images, black and white, or any other settings and extracts data with high accuracy. Algodocs can process scanned images with as low a dpi as 75. If you have scanned PDFs or images with low quality, then Algodocs is the right solution for you. You may start a free subscription right now and test your own scanned documents since we offer a free subscription (forever) with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. Please read our article on the basic steps for table extraction from documents here: Extract tables from PDF and scanned documents Algodocs: the best software tool to extract tables from scanned PDFs and images Consider the portions of the scanned documents below and the tables that Algodocs extracted from them. Example #1 Sample scanned image with low-quality (black and white) Extracted table by Algodocs. Example #2 Extracted table by Algodocs As you can see, the accuracy of Algodocs is perfect even with low-quality scans. However, there are cases when scanned images may cause Algodocs to make mistakes concerning small characters such as punctuation or other symbols (points, commas, date separators, etc.). Let’s have a look at the example below with a scanned image and see what Algodocs could extract from it. The extracted table from the above-scanned image is shown below. As you can see, there are numbers that are extracted with wrong decimal separators (indicated in red circles), i.e. a decimal point is mistakenly recognized as a comma. This is due to the dark background that some rows have on the image. With the help of flexible extracting rules of Algodocs, the workaround is quick and simple. Whenever you have low-quality scanned PDFs of images, we always advise you to follow the steps explained below. Step1. Remove all points and commas from the numbers We apply the ‘Search & Replace’ filter in Algodocs by using regular expressions as the search type. We apply this rule to all the columns in the example below, but you can restrict this rule to a specific column when needed. In order to find all dots or commas we use .|, as the search term and we leave empty the second field (replace by this), since we simply want to remove them. Step 2. Convert all numbers to their previous format Since we removed all points and commas from numbers, they actually increased, i.e. multiplied by 100 we can say (2,378.63 became 237863). Therefore, since we know that our numbers had 2 decimal places, we can divide all numbers by 100 to get the original numbers. The ‘Arithmetic Operation’ filter helps us implement exactly this. We divide numbers by 100 in the last column as shown in the example below. You may apply this filter to other columns too. That’s it. We got numbers in their original form with 100% accuracy! The same approach can be applied to other symbols when you have documents with a low quality. Please, contact us if you need any assistance. You Might Also Like How to Extract Handwritten Data from PDFs with Algodocs? Table of Contents Want to learn how to extract handwritten data from PDFs easily for your business documents? Algodocs is a powerful AI platform… Shubhankar Biswas October 13, 2025 Data Extraction for Legal Industry : How Intelligent Document Processing (IDP) Can Transform Legal Industry Document Workflow The global law and legal services industry is expected to reach $1,591.56 billion by the end of 2032, according to a report. The legal industry… Shubhankar Biswas October 12, 2025 Extract Tables from Images with AlgoDocs Extract Tables from Images with AlgoDocs One might find themselves overwhelmed by a deluge of paperwork—orders, checks, articles—all containing… Ibrahim Nalbant June 20, 2024 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

Artificial Intelligence, Data Extraction

AI Data Extraction Checklist: Transform Business with Algodocs

Back to Blog Table of contents On this page AI Data Extraction Checklist: Transform Business with Algodocs Home › Blog › Artificial Intelligence › AI Data Extraction Checklist: Transform Business with Algodocs Categories Artificial Intelligence Data Extraction Tags bank ocr deep learning digital transformation Finance ocr invoice invoice process KYC loan forms packing list ocr pdf to text sea waybill ocr web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 09:25 Updated July 23, 2026, 07:33 What is AI Data Extraction? Let’s discuss data – a lot of data. Modern-day businesses are losing themselves in the sea of information. Whether it is an invoice, a contract, a weekly report, or a form, paper documents are still part of everyday life. Extracting data from such documents is a tedious, repetitive, and painful process if done manually. This is where AI data extraction comes to the rescue. It is a game-changer for anyone who has to work with several papers. Incorporating AI means data entry activities are done efficiently and there’s an increase in the accuracy and productivity of your business. Algodocs is a perfect example of an AI data extraction tool. Why? Let’s find out. Understanding Your Data: The Foundation for Successful AI Data Extraction Before you set loose the AI on your documents, I thought it’s best to discuss a little about your data. It is essential to know what type of data you are going to extract and what the process is going to be like. Identify Your Data Sources First things first: where do you store your data? The first action plan is to identify this source. Is it hidden in files and papers or dispersed in different online sites as in virtual archives? You’ll find your data in various forms: Manual records or documents that include invoices, contracts, and reports, among others. Documents in PDF files, Word documents, Excel, images, and many others. Databases that store many formats of data about any department in an organization. Assessing Data Quality Data quality is super essential for accuracy in the extraction process. Ensure that you go through your assembled data to determine whether it meets the standard of completeness, consistency, and accuracy. Check that all required data items are included. Check whether the formats, units of measurement, and terms used for data are consistent. Validating steps where errors, inconsistencies, or outliers will affect the outcome must be adopted diligently. Dedicating time to data preparation will enable laying down the key fundamentals of an AI data extraction project. Optimizing the extraction results of Algodocs, the company provides you with tools for evaluating data quality and detecting potential problems. Choosing the Right AI Data Extraction Tool Picking the right tool to extract AI data is critical to the success of any AI project. As we have seen, there are numerous strategies out there; that is why it is crucial to define your requirements precisely and compare tools based on the crucial factors. Critical Considerations for Tool Selection Accuracy: The whole idea of training an AI in the first place is to increase accuracy, isn’t it? Search for the tool with favorable accuracy characteristics, especially in the case of processing intricate and diversely formatted documents. Don’t forget about tables, crazy handwriting, or low-quality pictures. Speed: It’s important, especially in dealing with large numbers of documents that are prevalent in the modern organization. However, time translates to costs, more so when handling big data at hand or any other business. Having a fast and efficient tool can save hours, if not days off of your time. Flexibility is very important, especially for those industries that anticipate expansion. Scalability: The tool should be designed for growth which includes a higher amount of input data and scalability of business over time. Document Types: Think about the countless supported document types with your tool (PDFs, images, Word, Excel, and more). Data Formats: Verify that the tool can export information according to your preferred choice format (CSV, XML, JSON, etc.). Integrations: This is very important as technology should be compatible with the current systems and applications. In other words, a single tool could essentially serve the purpose. But if a tool can integrate with other existing systems, then that’s ideal. It integrates with frequently used business applications such as CRMs, ERPs, and data warehouses. Pricing: Analyze cost distribution for various pricing structures considering your estimated budget. Customer Support: Customer support should be reliable and available for providing technical support and answering questions at all times. Types of AI Data Extraction Tools Cloud-Based Tools AI data extraction in cloud-based is also beneficial since it is scalable, easily accessible, and mostly cheaper. These tools are stored at service providers’ central servers, and users only need an Internet connection to use them. Examples: Google Cloud Document AI, Amazon Textract, ABBYY Cloud OCR, and Algodocs. On-Premise Tools On-premise solutions allow necessary control over infrastructure, but they can be more expensive. These tools are deployed and run on the organization’s own IT systems and infrastructure. It is worth mentioning that Algodocs is a web-based tool; however, it can also be used On-premise. Examples: Kofax, OpenText, and Algodocs. Open-Source Tools Using and adapting open-source AI data extraction tools is more flexible and customizable, but implementing these tools requires technical skills. Examples: Tesseract OCR, OpenCV Mobile Apps Mobile applications usually reside on document capture and basic data mining capabilities. This is perfect for small data snippets or impromptu snapshots of information gathering. However, such tools may not work well when a large quantity of structured content, such as tables, is involved. Not to forget that handwriting style and layout complexities can cause the accuracy of such tools to drop. Examples: Google Lens or Microsoft Office Lens can scan your document and convert the text into a digital format. Making an Informed Decision Considering the following aspects and your organization’s requirements, you can choose the right AI data extraction tool for your organization. Feature Cloud-Based On-Premise Open-Source Mobile Apps Accuracy High High Varies Varies Speed High High Varies

Algodocs

What Is Intelligent Document Processing (IDP) and How Can It Revolutionize Your Business in 2026?

Back to Blog Table of contents On this page What Is Intelligent Document Processing (IDP) and How Can It Revolutionize Your Business in 2026? Home › Blog › Algodocs › What Is Intelligent Document Processing (IDP) and How Can It Revolutionize Your Business in 2026? Categories Algodocs Tags ai platform for data extraction algodocs deep learning guide IDP intelligent document processing machine learning ocr api By Shubhankar Biswas Published October 12, 2025, 18:51 Updated July 13, 2026, 06:16 In today’s digital age, data is crucial for every organization. However, a significant portion of this data resides within unstructured documents, such as contracts, invoices, forms, emails, and more. Manually processing data from these documents is time-consuming, error-prone, and costly for any business. Intelligent Document Processing (IDP) has emerged as a game-changing solution by leveraging the power of artificial intelligence (AI) and machine learning (ML). Document workflows and data extraction from these unstructured documents have become very easy. This comprehensive guide will explore the landscape of IDP platforms, their benefits, how they can be applied across various industries, key considerations for choosing an IDP solution, emerging trends, and how Algodocs can empower your business. What is Intelligent Document Processing (IDP)? A Deep Dive The IDP (Intelligent Document Processing) market is expected to reach $46.59 billion USD by the end of 2035, according to a report. As businesses heavily rely on, extraction, sorting, and managing data. These types of operations require a robust and reliable tool to that can provide useful insights about business metrics. That’s why the need for intelligent document processing tools is rising day by day. So, what is intelligent document processing? IDP is a sophisticated technology that automates the extraction, classification, and processing of data from various document types, regardless of format or structure. It achieves this by combining several core technologies: Optical Character Recognition (OCR): OCR converts images of text into machine-readable text. Modern OCR, often referred to as Intelligent Character Recognition (ICR), goes beyond basic character recognition by using deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This allows it to handle complex layouts, handwritten text, low-quality images, multiple languages, and specialized fonts used in industries like healthcare (medical prescriptions) and law (legal documents). Accuracy is often measured using metrics like character error rate (CER) and word error rate (WER), with advanced systems achieving very low error rates. Natural Language Processing (NLP): NLP analyzes the extracted text to understand its meaning and context. It employs techniques like tokenization (breaking text into individual words or phrases), stemming and lemmatization (reducing words to their root form), Named Entity Recognition (NER) (identifying specific entities like names, dates, and locations), Part-of-Speech (POS) tagging (identifying the grammatical role of each word), sentiment analysis (determining the emotional tone of the text), topic modelling (discovering underlying topics within a collection of documents), text summarization (creating concise summaries of longer texts), relationship extraction (identifying relationships between entities), and semantic analysis (understanding the meaning of words and phrases in context). Machine Learning (ML): ML is the engine that drives IDP’s adaptability and continuous improvement. Supervised learning involves training the system on labelled data to recognize specific document types and extract relevant fields. Unsupervised learning helps discover patterns and structures in unlabelled data for improved classification and clustering. Reinforcement learning allows the system to learn through feedback and iterative improvement. Training data quality and quantity are crucial for model accuracy. Techniques like cross-validation and hyperparameter tuning are used to optimize model performance. Active learning allows the system to request human input for ambiguous cases, further improving its accuracy over time. Computer Vision: Computer vision enables IDP to “see” and interpret visual elements within documents. Techniques like image classification (categorizing images), object detection (identifying specific objects within images), image segmentation (dividing an image into multiple segments), table and form extraction (accurately extracting data from structured tables and forms), barcode and QR code recognition (automating data capture from barcodes and QR codes), signature verification (authenticating signatures), and logo detection (identifying company logos) are used. Robotic Process Automation (RPA): RPA acts as the orchestrator for IDP workflows, automating downstream processes based on the extracted data. It integrates IDP with other enterprise systems like CRM, ERP, and ECM, automating data validation, routing documents to appropriate departments, and triggering subsequent actions. IDP Workflow: A Detailed Breakdown: Document Ingestion: Documents are ingested through various channels: scanning, uploading files, APIs, email attachments, and more. Pre-processing: Images are optimized for OCR through techniques like noise reduction, skew correction, and image enhancement. OCR and Text Extraction: OCR extracts text from the document. NLP and Data Understanding: NLP analyzes the extracted text. Data Extraction and Validation: Relevant data is extracted and validated against predefined rules or databases. Human-in-the-Loop (HITL): Human reviewers handle exceptions and complex cases where the system has low confidence. Data Output and Integration: Extracted data is delivered in structured formats (CSV, JSON, XML) or directly integrated into business applications. The Benefits of IDP: Quantifiable Impacts Efficiency Gains: IDP can dramatically reduce document processing time, often by up to 90%. For example, processing hundreds of invoices that previously took several days can be completed in just a few minutes or hours. This increased throughput allows businesses to handle higher volumes of documents without increasing staffing. Cost Reduction: By eliminating manual data entry and reducing errors, IDP can lower operational costs by up to 70%. Reduced rework, fewer errors requiring correction, and optimized resource utilization contribute to significant cost savings. Accuracy Improvement: IDP achieves data extraction accuracy rates of 99% or higher, significantly minimizing data entry errors and improving data quality and consistency. This reduces costly downstream errors and improves compliance. Enhanced Security and Compliance: IDP systems offer robust security features like data encryption, access control, and audit trails, ensuring compliance with data privacy regulations like GDPR, HIPAA, and others. Improved Customer Experience: Faster processing times translate to quicker service delivery, leading to improved customer satisfaction. For example, faster loan approvals or insurance claims processing can significantly enhance the customer

insurance data extraction
Data Extraction, Insurance Data Extraction

Insurance Data Extraction: Automating Policy and Claim Processing with AI

Back to Blog Table of contents On this page Insurance Data Extraction: Automating Policy and Claim Processing with AI Home › Blog › Data Extraction › Insurance Data Extraction: Automating Policy and Claim Processing with AI Categories Data Extraction Insurance Data Extraction Tags ai platform for data extraction algodocs character recognition deep learning IDP Insurances Data Extraction intelligent document processing By Shubhankar Biswas Published October 12, 2025, 18:20 Updated July 23, 2026, 05:31 The modern insurance sector operates on massive volumes of information and insurance data extraction is a crucial part of insurance industry business process. Every single day, insurance carriers, third-party administrators, brokers, and agencies process thousands of files. These files range from policy applications and loss notices to medical bills, repair estimates, legal notices, and property appraisal reports. Within this ocean of paperwork lies the essential information required to underwrite risks, calculate premiums, settle claims, detect fraud, and maintain regulatory compliance. However, a significant challenge facing the insurance industry today is that up to eighty percent of this critical information arrives in unstructured or semi-structured formats. Scanned PDF files, digital images, physical paper forms, email attachments, and handwritten adjuster notes cannot be directly read or processed by traditional enterprise databases. Historically, companies relied heavily on manual data entry to transfer details from incoming documents into core operational platforms like claims management systems or policy administration suites. Manual processing creates severe bottlenecks. It inflates operational expenses, slows down service delivery, introduces human error, and frustrates policyholders who expect immediate digital service. To overcome these challenges, progressive insurance providers are transitioning toward automated insurance data extraction. Driven by advances in AI OCR, Machine Learning, and Intelligent Document Processing, modern extraction tools allow organizations to transform messy, unformatted files into structured, machine-readable digital data in seconds. This comprehensive guide explores the fundamentals of automated insurance data extraction, examining the types of documents processed, the hidden costs of manual workflows, the business benefits of artificial intelligence, and the leading software solutions shaping the market. What is Insurance Data Extraction? Insurance data extraction is the automated technology process of reading, identifying, capturing, and converting information from paper or digital insurance records into structured digital data formats. Instead of requiring human workers to manually read documents and type values into software screens, an automated insurance data extraction workflow ingests files, interprets their contents, extracts specific data fields, and exports clean data directly into core databases or enterprise applications. The insurance data extraction workflow relies on an integrated stack of advanced technologies: Document Ingestion: The system collects inbound files from multiple channels, including email inboxes, mobile application uploads, scanned physical mail, cloud storage, or secure web portals. Optical Character Recognition (OCR) and AI OCR: Legacy OCR converts scanned document images into text strings. Next generation AI OCR goes further by recognizing complex handwriting, low-resolution scans, and non-standard typography. Intelligent Document Processing (IDP): Combining artificial intelligence with natural language processing, Intelligent Document Processing understands the context and semantic meaning of words. It recognizes that a dollar figure located next to the word deductible represents a specific financial field rather than a premium payment. Machine Learning and Field Validation: Machine learning algorithms continuously improve extraction accuracy over time. Automated validation rules cross check extracted policy numbers, dates, and mathematical totals against internal enterprise databases to verify accuracy. Data Export: Clean, structured data is formatted into structured payloads such as JSON, XML, or CSV, and delivered directly to core policy or claims platforms via APIs. Through AI insurance data extraction, carriers can automate repetitive administrative steps, reduce turnaround times, and establish seamless insurance workflow automation across every department. Types of Documents Used in Insurance Industry for Insurance Data Extraction Insurance carriers handle a vast assortment of forms, reports, and contracts. Each document type possesses unique layouts, terminology, and data extraction requirements. Automated insurance document processing systems are designed to process diverse document types efficiently. 1. Policy Documents and Applications Policy applications and insurance binders represent the foundational agreements between policyholders and carriers. Extracting accurate data during policy origination is crucial for setting terms and issuing coverage. Key documents include: ACORD 125 Applications: The standard commercial insurance application form containing applicant business details, policy effective dates, location details, and requested coverage limits. Policy Schedules and Declarations Pages: Summary pages detailing coverage types, policy numbers, named insureds, endorsement listings, and premium amounts. Endorsement Forms: Amendments to existing policy contracts that modify coverage terms, add new insured assets, or change liability limits. Accurate policy data extraction ensures that customer profiles, coverage clauses, and premium figures match internal records without requiring manual entry. 2. Insurance Claims and Loss Notices When policyholders suffer a loss, rapid claims intake is essential for customer satisfaction. Automated insurance claims data extraction parses critical details from early claims documentation: First Notice of Loss (FNOL) Reports: Initial notifications submitted by policyholders or agents detailing how, when, and where an incident occurred. ACORD Loss Notices: Standardized notices such as the ACORD 2 Automobile Loss Notice or ACORD 3 Property Loss Notice, capturing policy details, driver information, and loss descriptions. Incident and Police Reports: Official law enforcement collision records containing narrative descriptions, driver statements, weather conditions, and citations. Automating insurance claims data extraction accelerates claim registration, enabling immediate assignment to claims adjusters or automated decisioning engines. 3. Medical Records and Healthcare Billing Forms Health, workers compensation, and personal injury protection claims involve complex clinical and billing records. These documents combine typed text, medical terminology, complex codes, and handwritten notes: CMS-1500 and UB-04 Forms: Standard medical billing claim forms used by physicians, medical practices, and institutional healthcare providers. Explanation of Benefits (EOB): Statements sent by health plans explaining covered services, approved amounts, deductible applications, and patient responsibility. Clinical Discharge Summaries and Doctor Notes: Narrative medical charts containing diagnoses, treatment histories, and surgical notes. Automated extraction engines capture critical diagnostic codes such as ICD-10, procedure codes such as CPT, billing line items, and provider identification numbers to streamline health claims auditing. 4. Underwriting Documents and Financial Statements Underwriters must review complex

Healthcare

Healthcare Data Extraction: Improving Healthcare Document Workflow with AI and IDP In 2025: A Case Study

Back to Blog Table of contents On this page Healthcare Data Extraction: Improving Healthcare Document Workflow with AI and IDP In 2025: A Case Study Home › Blog › Healthcare › Healthcare Data Extraction: Improving Healthcare Document Workflow with AI and IDP In 2025: A Case Study Categories Healthcare Tags algodocs data extraction deep learning healthcare intelligent document processing machine learning ocr api By Shubhankar Biswas Published October 12, 2025, 16:30 Updated July 20, 2026, 10:11 Healthcare data extraction has always been a challenge for hospitals, healthcare providers, and insurance companies. Extracting data from multiple documents was a complex task. However, the advent of AI and Intelligent Document Processing (IDP) technologies has significantly impacted how the healthcare industry processes data from various healthcare documents. The healthcare industry is a vast sea of data. Every patient interaction, medical procedure, and insurance claim relies on information. This data, locked within various document formats, holds immense potential to improve patient care, streamline healthcare operations, and drive innovation. However, extracting this valuable information from diverse healthcare documents has traditionally been laborious, error-prone, costly, and inefficient. This is where technologies like IDP, AI, Machine Learning (ML), Large Language Models (LLM), and Optical Character Recognition (OCR) come into play. With these innovative technologies, we have improved data extraction for the healthcare industry. According to a recent report, the healthcare industry is a USD 5,862.1 billion industry and is expected to reach USD 9,245.8 billion by 2033. Another report by Deloitte suggests that AI technologies can save USD 360 billion in costs in the USA by next year. The healthcare industry generated up to 2.3 zettabytes of data worldwide in 2020. In this blog, we will discuss how AI is changing data extraction for the healthcare industry across the globe and the technologies and tools behind this technological advancement. The Diverse Landscape of Healthcare Documents The healthcare industry is inundated with various documents, each containing critical information crucial for patient care, administration, and research. Understanding these documents is the first step in effectively leveraging data extraction. Let’s delve into the key document types: Medical Bills Medical bills are more than just invoices; they are detailed records of services rendered to a patient. They contain crucial information, including: Patient Demographics: First Name, Last Name, address, date of birth, insurance, and other crucial details. Provider Information: Name, address, National Provider Identifier (NPI), etc. Service Details: Procedure codes (CPT/HCPCS), diagnosis codes (ICD-10), dates of service, quantities, and descriptions. Charges and Payments: Itemized charges, adjustments, insurance payments, patient responsibility, payment methods, tax details, etc. Efficiently extracting data from medical bills is vital for revenue cycle management, claims processing, cost analysis, and identifying trends in healthcare spending. Accurate data extraction ensures timely reimbursements, reduces claim denials, and provides insights into cost-effective care delivery. Handwritten Bills Despite the growing adoption of Electronic Health Records (EHRs), handwritten bills persist in many healthcare settings, particularly in smaller practices or during field visits. Many developing Asian countries, as well as developed countries, still rely on handwritten bills. These bills often contain information such as: Patient Information: Basic details like first name, last name, sometimes age or contact information, dates, etc. Treatment Details: Brief descriptions of services provided, often in abbreviated form. Charges: Handwritten amounts for each service or an overall total. Extracting data from handwritten bills presents a unique challenge due to variations in handwriting styles, abbreviations, and the potential for smudges or illegible entries. Advanced OCR coupled with Natural Language Processing (NLP) is essential for accurate data extraction from these documents. Patient Forms Patient forms are the cornerstone of patient intake and data collection. They gather essential information that forms the basis of a patient’s medical record. Sometimes these forms are filled with handwritten data, which presents a challenge for data extraction. Though the majority of patient forms are computer-generated, in many cases, they are handwritten. Common types of patient forms include: Registration Forms: Collect basic demographics, insurance information, and emergency contacts. Medical History Forms: Document past illnesses, surgeries, allergies, medications, and family history. Consent Forms: Obtain patient authorization for treatment, procedures, or release of information. HIPAA Forms: Ensure compliance with the Health Insurance Portability and Accountability Act regarding patient privacy. These forms often contain valuable information such as first name, last name, address, body weight, blood group details, current health issues, and previous diagnoses. They often contain a mix of structured (checkboxes, multiple-choice) and unstructured (free-text) data. Effective data extraction from these forms relies on advanced form recognition and NLP techniques to capture both types of information accurately. Health Insurance Documents Health insurance documents, including Explanation of Benefits (EOBs) and insurance cards, are crucial for understanding a patient’s coverage, verifying eligibility, and processing claims. They contain: Insurance Cards: Provide member ID, group number, plan type, contact details, and other information. Explanation of Benefits (EOBs): Detail how a claim was processed, including allowed amounts, co-pays, deductibles, and reasons for any denials. Data extraction from insurance documents enables accurate billing, reduces claim rejections, and helps patients understand their financial responsibilities. It also provides valuable data for insurance companies to analyze utilization patterns and manage risk. Other Vital Documents Beyond these core document types; the healthcare ecosystem encompasses a multitude of other documents: Lab Reports: Contain results of diagnostic tests, including blood work, imaging, and pathology reports. Prescription Forms: Detail medication name, dosage, frequency, and refill information. Referral Forms: Facilitate specialist consultations and continuity of care. Discharge Summaries: Provide a comprehensive overview of a patient’s hospital stay, including diagnosis, treatment, and follow-up instructions. Clinical Notes: Document physician observations, assessments, and treatment plans. Each document plays a unique role in patient care and administration. This diverse range provides a holistic view of a patient’s journey, enabling better care coordination, research, and population health management. How AI, ML, and IDP Leverage Healthcare Data Extraction The traditional approach to extracting data from these diverse healthcare documents has been manual data entry, a process fraught with challenges. However, the emergence of AI, ML, and IDP has revolutionized data extraction, offering a more efficient, accurate, and scalable solution. Try Algodocs AI data extraction platform to extract data from variou types of documents. Sign up

PDF

How To Convert PDF to Text Using AI: A Comprehensive Guide For 2025

Back to Blog Table of contents On this page How To Convert PDF to Text Using AI: A Comprehensive Guide For 2025 Home › Blog › PDF › How To Convert PDF to Text Using AI: A Comprehensive Guide For 2025 Categories PDF Tags ai platform for data extraction algodocs data extraction from images deep learning pdf to text By Shubhankar Biswas Published September 1, 2025, 08:25 Updated July 21, 2026, 07:03 Challenges of Converting How To Convert PDF to Text Using AI: A Comprehensive Guide For 2025PDF to Text Using AI PDF files remain the backbone of sharing textual data across various departments in businesses today. From contracts and invoices to research papers and reports, they are the preferred format for information storage and exchange. However, extracting valuable text from these documents can be a tedious and time-consuming task. This is where AI-powered solutions revolutionize PDF to text conversion. A PDF can exist in different formats, such as scanned documents, scanned images, or native PDFs that are easily searchable. Extracting data from scanned images or documents requires advanced AI technology to ensure accuracy and efficiency. In today’s business landscape, AI-based OCR technology is transforming how we interact with PDFs by offering seamless text extraction, enhanced accuracy, and increased efficiency. This guide explores the complexities and challenges of converting PDF to text using AI, along with its benefits, applications, and future advancements. Challenges of Converting PDF to Text Using AI PDF files come in different types: Native PDFs: These files allow easy editing and copying of text. Scanned PDFs: These contain embedded images of text, making manual extraction difficult.The challenge arises when dealing with scanned PDFs or image-based documents, where data cannot be directly copied or edited. Extracting information manually from thousands of PDFs is time-consuming, prone to errors, and inefficient. Traditional OCR tools offer basic character recognition but struggle with complex layouts, varied fonts, and handwritten text, often leading to inaccurate results. This highlights the need for an advanced AI-powered solution to convert PDF to text using AI efficiently. AI-Based OCR: Transforming PDF to Text Conversion Intelligent Document Processing (IDP) combines Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to enhance traditional OCR capabilities. Instead of merely recognizing characters, AI-powered solutions understand document structures, identifying elements like headings, paragraphs, tables, and images while preserving formatting and ensuring high accuracy. AI-powered OCR tools convert PDFs to text with unmatched precision, making document processing easier and more efficient. How AI-Based OCR Works to Convert PDF to Text The efficiency of AI-based OCR lies in its sophisticated algorithms. Here’s how it works: Document Preprocessing: The PDF undergoes enhancement to remove noise, correct skew, and improve readability. Layout Analysis: AI analyzes the document structure, recognizing sections, columns, and tables. Enhanced OCR with AI: Unlike traditional OCR, AI-driven engines recognize a wide range of fonts, styles, and even handwritten text with high precision. Natural Language Processing (NLP): AI interprets the extracted text, corrects errors, and ensures contextual accuracy. Text Extraction & Formatting: The extracted text is presented in readable formats like plain text, HTML, or structured data (e.g., JSON) while preserving its original formatting. Benefits of Using AI to Convert PDF to Text With our Algodocs Generative AI Feature You Can Convert PDF To Text With Few Prompts. Try Our Free App Today AI-powered solutions provide numerous advantages, including: High Accuracy: Minimizes errors and reduces the need for manual corrections. Efficiency: Automates extraction, saving time and resources. Enhanced Data Accessibility: Converts PDFs into text for easy analysis and decision-making. Scalability: Processes large volumes of PDFs quickly and efficiently. Cost Savings: Reduces labor costs and boosts productivity. Seamless Integration: Extracted text can be integrated into various applications and systems. Applications of AI-Based PDF to Text Conversion AI-powered OCR tools are used across industries: Legal: Extracts text from contracts and case files. Finance: Processes invoices and financial reports. Healthcare: Extracts data from medical records and patient forms. Education: Converts research papers and textbooks into accessible formats. Government: Processes official documents and forms. Research: Extracts insights from scientific publications. Data Entry Automation: Automates data extraction from forms and documents. Content Management: Makes PDF content searchable and accessible. Key Challenges and Considerations in Converting PDF to Text Using AI While AI-based OCR significantly improves PDF to text conversion, some challenges remain: Complex Layouts: Multi-column formats and tables can still pose difficulties. Low-Quality Scans: Poorly scanned documents and handwritten text may require additional processing. Security & Privacy: Choose AI tools that prioritize data security. Cost: Pricing varies based on features and processing volume. Choosing the Right AI Tool to Convert PDF to Text When selecting an AI solution, consider: Accuracy: High precision in text extraction. Speed: Quick and efficient processing. Scalability: Ability to handle large document volumes. Features: Support for table extraction, formatting preservation, and multiple languages. Security: Strong data protection measures. Cost: Budget-friendly pricing models. Integration: Compatibility with existing workflows and systems. Algodocs AI: Simplifying PDF to Text Conversion One of the most powerful AI-based OCR tools is Algodocs AI. It combines AI and traditional OCR to extract data from various document types, including PDFs, scanned images, and complex layouts. Algodocs AI ensures high accuracy and efficiency, allowing users to extract text, tables, and structured data with ease. It simplifies PDF to text conversion, making it effortless to unlock valuable information within your documents. Algodocs AI makes it easier than ever to convert PDFs to text using AI, streamlining business workflows. The Future of AI in PDF to Text Conversion The field of AI-based OCR is constantly evolving. Future advancements will further enhance accuracy, efficiency, and functionality. With the integration of Robotic Process Automation (RPA) and cloud computing, AI solutions will enable seamless automation and data analysis across industries. Conclusion Converting PDFs to text using AI is a game-changing innovation, streamlining workflows, reducing errors, and improving data accessibility. With applications across multiple industries, AI-powered solutions like Algodocs AI are leading the way in automated document processing. As businesses become increasingly data-driven, leveraging AI to convert PDF to text will remain a crucial tool for unlocking and utilizing

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